Firstly, Iceland is not ‘randomly’ testing people. People are signing up to be tested voluntarily. That population is likely to contain a larger fraction of people who have reason to think they were exposed or feel sick. Thus 0.8% is an overestimate of the fraction of the population that has been infected.
Technically true—and this is why in the earlier version of this on my blog, I used the word ‘random-ish’.
Obviously the test is voluntary, but it’s also clearly designed to estimate prevalence:
″ This effort is intended to gather insight into the actual prevalence of the virus in the community, as most countries are most exclusively testing symptomatic individuals at this time,” said Thorolfur Guðnason, Iceland’s chief epidemiologist to Buzzfeed.
During this time of year less than 10% of the population has symptoms, so if it was a random sampling of only that subset, we would predict at most 400 cases, so we can reject that.
Nonetheless, I think this does justifying widening the prediction of #infections and moving the mean down a bit.
Secondly, the asymptomatic period is on average a week or so for those who develop symptoms, with hospitalization often occurring upwards of a week after symptoms, and death often occurring more than 2 weeks after symptoms. .. .This thing is damn infectious and still expanding, it is not anywhere near a steady state anywhere
Did you actually look at the Iceland data? They entered a linear regime (midpoint of the sigmoid) about 10 days ago, which defeats the brunt of this argument. Additionally the vast majority of the cases were discovered through normal testing after symptoms present, so subtract a week from your timeframe. And finally I already did attempt to predict future deaths based on ICU. I also considered adding another predicted death from the # in hospital now, but it’s unclear if that is distinct from ICU or not.
Ultimately though only time will tell, but I find it unlikely they are going to get up to dozens of deaths without also growing case count.
According to links in the above writings, 0.5% of flu cases in the 20-45 age group result in hospitalization compared to 10% in the over 65 age group, and taking population into account that results in ~7x as many flu over-60 hospitalizations than 20-45. Current American test results, however, have ~2x the over-65 covid hospitalizations as 20-45 hospitalizations.
The hospitalization rate that matters is (hosp | infected), not (hosp | tested). You are comparing the estimated (hosp | infected) curve of influenza to the (hosp | tested) curve of COVID-19, which is a unit mismatch. For that comparison to be meaningful you need to first correct for age-specific (tested | infected) ratio.
And data is indicating that surviving ICU stays for this disease are ~3x as long as ICU stays for flu.
I did look at the Iceland data. I divided the NUHI positive tests by the total tests, and saw a very noisy upwards-trending line in the fraction of positive test results.
As for hospitalizations, I was comparing the age distribution of hospitalizations for flu and confirmed covid. I found that the ratio of 20-45:65+ hospitalizations for flu was 1:7, and that the same ratio for covid was 1:2. Assuming a similar age distribution for actual infections, this means a larger fraction of young people is coming down with severe disease.
Assuming a similar age distribution for actual infections, this means a larger fraction of young people is coming down with severe disease.
Disease severity increases with age, and testing probability increases with severity and thus age (in most places). Thus the ratio p(tested | infection) is age skewed and typically much lower for younger ages.
After adjusting by dividing by age dependent p(tested | infection) you can correct that skew and you probably get something more similar to influenza hosp rate curve.
So again you aren’t comparing even remotely the same units and it’s important to realize that.
As for hospitalizations, I was comparing the age distribution of hospitalizations for flu and confirmed covid. I found that the ratio of 20-45:65+ hospitalizations for flu was 1:7, and that the same ratio for covid was 1:2. Assuming a similar age distribution for actual infections, this means a larger fraction of young people is coming down with severe disease.
Age distribution of estimated hospitalizations for flu? or confirmed? (It seems difficult to get the latter) Source?
′ New, amazing data from New York, as of April 13.
Hardly an unbiased sample, but of 200+ pregnant women coming into a hospital to give birth that were blanket-tested, 15.3% tested positive.
Of this set of positive tests, only 12% of them were symptomatic on admission, and a further 10% developed symptoms over the course of their 2-day-long stays bringing it to a total of 22% symptomatic upon discharge. Presumably already-symptomatic women were more likely to be in the hospital already.
Doing a little armchair epidemiology. Let’s assume that half of the deaths of currently infected people have happened, due to the lockdown extending the doubling time from three days to more than a week. We get:
~8000 deaths * 2 / (15.3% of 8 million) = 1.3% infection to mortality rate.
If we assume that there were more symptomatic women who didn’t show up to normal birthing due to going to the hospital for COVID symptoms, we get a lower death rate. If 20% of the total population is infected, we get a 1% mortality rate.
Compare this to what I wrote 21 hours ago, based on serology data from Italy and Germany:
‘From this, I estimate that at least 10% and possibly up to 20% of New York City has been infected, given the delay between infections and deaths. (100*8000 = 800,000, out of about 8 million) ’
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Adding here, that if you assume there are people who would have previously tested positive and recovered, it goes down a bit more. Most places that we get good data these days are converging on a 0.5% to a 0.7% mortality rate, so I suspect that’s also a contributor.
Technically true—and this is why in the earlier version of this on my blog, I used the word ‘random-ish’.
Obviously the test is voluntary, but it’s also clearly designed to estimate prevalence:
″ This effort is intended to gather insight into the actual prevalence of the virus in the community, as most countries are most exclusively testing symptomatic individuals at this time,” said Thorolfur Guðnason, Iceland’s chief epidemiologist to Buzzfeed.
During this time of year less than 10% of the population has symptoms, so if it was a random sampling of only that subset, we would predict at most 400 cases, so we can reject that.
Nonetheless, I think this does justifying widening the prediction of #infections and moving the mean down a bit.
Did you actually look at the Iceland data? They entered a linear regime (midpoint of the sigmoid) about 10 days ago, which defeats the brunt of this argument. Additionally the vast majority of the cases were discovered through normal testing after symptoms present, so subtract a week from your timeframe. And finally I already did attempt to predict future deaths based on ICU. I also considered adding another predicted death from the # in hospital now, but it’s unclear if that is distinct from ICU or not.
Ultimately though only time will tell, but I find it unlikely they are going to get up to dozens of deaths without also growing case count.
The hospitalization rate that matters is (hosp | infected), not (hosp | tested). You are comparing the estimated (hosp | infected) curve of influenza to the (hosp | tested) curve of COVID-19, which is a unit mismatch. For that comparison to be meaningful you need to first correct for age-specific (tested | infected) ratio.
Source?
I did look at the Iceland data. I divided the NUHI positive tests by the total tests, and saw a very noisy upwards-trending line in the fraction of positive test results.
As for hospitalizations, I was comparing the age distribution of hospitalizations for flu and confirmed covid. I found that the ratio of 20-45:65+ hospitalizations for flu was 1:7, and that the same ratio for covid was 1:2. Assuming a similar age distribution for actual infections, this means a larger fraction of young people is coming down with severe disease.
As for ICU periods, doctors are reporting that many covid patients require a ventilator for 1-2 weeks. https://www.nbcnews.com/health/health-news/what-ventilator-critical-resource-currently-short-supply-n1168641
I am looking for the resource I read yesterday that the typical flu ventilation period was 3-4 days.
Disease severity increases with age, and testing probability increases with severity and thus age (in most places). Thus the ratio p(tested | infection) is age skewed and typically much lower for younger ages.
After adjusting by dividing by age dependent p(tested | infection) you can correct that skew and you probably get something more similar to influenza hosp rate curve.
So again you aren’t comparing even remotely the same units and it’s important to realize that.
Age distribution of estimated hospitalizations for flu? or confirmed? (It seems difficult to get the latter) Source?
Copypasting myself from another thread:
′ New, amazing data from New York, as of April 13.
Hardly an unbiased sample, but of 200+ pregnant women coming into a hospital to give birth that were blanket-tested, 15.3% tested positive.
Of this set of positive tests, only 12% of them were symptomatic on admission, and a further 10% developed symptoms over the course of their 2-day-long stays bringing it to a total of 22% symptomatic upon discharge. Presumably already-symptomatic women were more likely to be in the hospital already.
Doing a little armchair epidemiology. Let’s assume that half of the deaths of currently infected people have happened, due to the lockdown extending the doubling time from three days to more than a week. We get:
~8000 deaths * 2 / (15.3% of 8 million) = 1.3% infection to mortality rate.
If we assume that there were more symptomatic women who didn’t show up to normal birthing due to going to the hospital for COVID symptoms, we get a lower death rate. If 20% of the total population is infected, we get a 1% mortality rate.
Compare this to what I wrote 21 hours ago, based on serology data from Italy and Germany:
‘From this, I estimate that at least 10% and possibly up to 20% of New York City has been infected, given the delay between infections and deaths. (100*8000 = 800,000, out of about 8 million) ’
---
Adding here, that if you assume there are people who would have previously tested positive and recovered, it goes down a bit more. Most places that we get good data these days are converging on a 0.5% to a 0.7% mortality rate, so I suspect that’s also a contributor.